This invention relates generally to machine and process control systems and more particularly to an automated self-learning diagnostic system for use with networked machines.
Diagnostics are utilized in machine control and process control systems to identify the cause of failure in a machine component or system from a failure symptom, as well as to predict the occurrence of a particular failure type from precursors. In a rapidly moving, competitive marketplace, the drive to develop and introduce new products as expeditiously as possible introduces difficulties in providing machine diagnostic capabilities, particularly for populations of networked machines. Diagnostics require an extended development cycle, since the determination of which components will fail in actual practice cannot be made until production level machines are available for testing purposes.
Current practices in developing diagnostic algorithms require extensive life-testing of components in order to determine nominal and threshold values for many critical parameters of the algorithms. The data collection can be tedious, error prone and sometime delay the timely introduction of new products. Worse, because of variability in manufacturing processes and machine operations in the field, newly launched products typically require continuous adjustment of alert levels in order to minimize unnecessary or missed alarms. For example, a fuser roll in a copy machine is generally replaced at a preset interval, which is initially determined by laboratory testing. Field experience has been known to show service intervals that vary significantly from the initial values.
In developing machine diagnostics, it would be useful to have an approach which utilizes machine data provided across a large number of machines connected on a digital information network such that the actual signals can be remotely observed.
The following U.S. Patents may be useful in providing additional background information on the use of diagnostic systems:
U.S. Pat. No. 5,123,017 to Simpkins et al. for “Remote Maintenance Monitoring System” teaches a remote maintenance monitoring system structured to capture failure data from a hardware device. The collected failure data is analyzed with an expert system to isolate the origin of the failure to facilitate maintenance of a monitored large-scale system.
U.S. Pat. No. 5,566,092 to Wang et al. for “Machine Fault Diagnostics System and Method” discloses a machine fault diagnostic system which employs a diagnostic network based on a neural network architecture. A hypothesis and test procedure based on fuzzy logic and physical bearing models operates to detect faults and analyze complex machine conditions.
U.S. Pat. No. 5,953,226 to Mellish et al. for “Control System Having an Application Function with Integrated Self Diagnostics” teaches a self-diagnostic system integrated into an application program for controlling a machine or process. The diagnostic program annunciates an event when the application program cannot execute a desired response by monitoring certain preselected and marked segments of the application program and the program allocates memory to save the result. When an abnormal occurrence in the segments results in the desired event not occurring, the diagnostic system determines what part of the logic expression controlling the event could not be completed. This information is saved and used for annunciating the reason for the desired event not happening.
U.S. Pat. No. 6,041,287 to Dister et al. for “System Architecture for On-Line Machine Diagnostics” discloses a machine diagnostic system which includes a host computer for determining a health state of a machine. A machine diagnostic module is adapted to be integrally mounted to a machine, with the machine diagnostic module operatively coupled to a network. The machine diagnostic module collects data relating to operation of the machine and preprocesses the data, with the host computer analyzing the preprocessed data to determine the health state of the machine.
U.S. Pat. No. 6,199,018 to Quist et al. for “Distributed Diagnostic System” teaches a system in which a plurality of local monitoring devices collect local information concerning various machines and process that information for diagnostic purposes. The local information collected is provided to a global processor that globally process the collected information to provide updated diagnostic parameters to the local monitoring devices.